Using Remote Sensing Methods to Monitor Soil Organic Carbon for Regenerative Agriculture and Carbon Sequestration. A Game-Changer for Nigeria Farmers

Regenerative agriculture and carbon sequestration are climate-smart concepts

becoming increasingly important as we face the challenges of climate change and

soil degradation. At Agroxchange Technology Services Limited, we are exploring

innovative ways to support these practices using earth observation data. One

exciting application is the use of earth observation to monitor soil organic carbon

(SOC) levels, providing valuable data for farmers and researchers alike. It offers a

revolutionary approach to monitoring SOC, enhancing regenerative farming

practices, and supporting carbon credit certification programs.?

Understanding Soil Organic Carbon and Its Importance?

Soil Organic Carbon(SOC) is the carbon stored within soil organic matter, crucial in maintaining soil health, fertility, and structure. It is also a key indicator of carbon sequestration, as soils act as carbon sinks that can trap atmospheric CO?. The higher the SOC levels, the more effective soil is at reducing greenhouse gases, enhancing food production, and combating climate change. ?

Regenerative Agriculture and SOC?

Regenerative agriculture involves farming techniques that restore soil health, biodiversity, and ecosystem services. These methods, such as cover cropping, crop rotation, reduced tillage, and agroforestry, improve SOC and foster long-term environmental sustainability. By building SOC, regenerative agriculture enhances soil’s capacity to store carbon, contributing to global efforts to mitigate climate change.?

Remote Sensing in SOC Monitoring?

Traditional SOC measurement methods, like soil sampling and lab analysis, are labor-intensive, costly, and time-consuming. However, remote sensing methods offer a faster, more cost-effective, and highly accurate alternative for monitoring SOC. vegetation health, soil moisture, and organic matter content, which are correlated with SOC levels

Key Advantages of Using Remote Sensing for SOC Monitoring

1. High-Resolution Imaging: Remote sensing provides high-resolution imagery that offers detailed information on vegetation health, soil moisture, and organic matter content, which are correlated with SOC levels.?
2. Non-Invasive Monitoring: Unlike traditional soil sampling, remote sensing-based monitoring does not disturb the soil, making it a more sustainable approach to tracking carbon levels over time.?
3. Real-Time Data Analysis: Remote sensing collected data can be processed in real-time, enabling quick insights into SOC fluctuations, and allowing farmers to make informed decisions about soil management.?
4. Cost Efficiency: By covering larger areas in a short amount of time, drones significantly reduce the costs associated with SOC measurement, particularly for large-scale regenerative agriculture projects.?
5. Precision Agriculture: Drones enable precision farming by providing granular data that allows for site-specific interventions. This leads to optimized inputs such as fertilizers and irrigation, boosting crop yields and increasing SOC storage.?

More studies have focused on predicting SOC content by using MLR (Multiple Linear Regression). The main reasons for widely using MLR are its straightforward intuitive interpretation, efficiency, and simplicity. However, the relationship between SOC content (as the output variable) and remote sensing data (as input variables) is mostly non-linear in nature. To overcome this issue, machine learning models, including artificial neural networks and support vector machines, have been reported to perform more accurate predictions than MLR for estimating and mapping SOC content. Satellite imagery, such as Sentinel-2 or Sentinel-1 data, can serve as key predictors in these models.

The objectives of recent studies include:

1. Deriving machine learning models using information extracted from Landsat-8, Sentinel-2, Sentinel-3, and MODIS images.
2. Improving the accuracy of SOC prediction through the spatio-temporal fusion of Sentinel-2 and Sentinel-3.
3. Examining the performance of machine learning models in arable and permanent agricultural lands.

Our approach combines SOC models with remote sensing to help carbon project developers, government entities, and verification bodies efficiently measure, estimate, and predict soil organic carbon. This method leverages the power of 140 predictors and significantly reduces the number of expensive physical soil samples needed for verification. The extensive range of predictors, including satellite imagery, greatly improves the accuracy of SOC estimations.

How Remote Sensing Helps Monitor Soil Organic Carbon?

Traditionally, measuring SOC has been a time-consuming and labor-intensive process involving field soil sampling and laboratory analysis. However, the remote sensing approach offers a more efficient alternative through

1. Multispectral imagery: Multispectral drone sensors and satellite imageries can capture data across various light wavelengths, including those sensitive to organic matter content.?
2. Large Area Coverage: Satellite imageries and drones can provide aerial coverage over several hectares, providing a comprehensive view of an entire farm, farming cluster, or region.?
3. Frequent Monitoring: Satellite imageries can be acquired daily or weekly in cases of Sentinel 2, while the ease of deployment allows for regular monitoring and tracking of changes in SOC levels over time.
4. Non-Invasive Method: Unlike traditional soil sampling, remote sensing methods do not disturb the soil, making it ideal for long-term studies.

The Process of SOC Monitoring Using Remote Sensing?

At Agroxchange we’re exploring new frontiers that feature innovative workflow that integrates remote sensing with machine learning to estimate Soil Organic Carbon(SOC) levels across farmlands.

1. Data Collection: Multispectral drones or satellite imagery capture high-resolution imagery over farmlands.
2. Ground Truthing: Soil samples are collected from select locations and analyzed in the lab to serve as reference data.
3. Image Processing: Multispectral images are processed to generate vegetation indices and additional relevant data layers.
4. Machine Learning: Ground truth data is used to train machine learning models to predict SOC levels based on the drone imagery.
5. Map Generation: The trained model is then applied to the entire surveyed area to produce detailed SOC maps.

This approach offers precision in monitoring and managing soil health, contributing to sustainable agriculture practices.

Carbon Sequestration and Carbon Credits Monitoring?

SOC is not just beneficial for soil health and agricultural productivity. It also plays a critical role in carbon sequestration, a process where carbon is stored in the soil for long periods, helping to offset CO? emissions.

In Nigeria, where agriculture is a key economic driver, remote sensing can support carbon sequestration efforts by providing the data required for carbon credit certification.

With accurate SOC measurements, Nigeria Flying Labs can help farmers quantify the amount of carbon sequestered in their soils and participate in carbon markets. This opens new revenue streams for farmers, incentivizes regenerative practices, and contributes to global carbon reduction targets. ?

Partnerships

Nigeria Flying Labs and Agroxchange Technology Services Limited, an ag-tech startup based in Nigeria that offers precision farming services and also provides data-driven and Environmental, Social, and Governance (ESG) compliant end-to-end farm management services have collaborated to explore this technological transformation. By integrating remote sensing or earth observation combined with compliance with Good Agronomy Practices(GAP) and extension services, into primary production,

Nigeria Flying Labs and Agroxchange Technology Services Limited will empower farmers to optimize their yield and incomes while boosting SOC, and unlocking carbon credit potential.

This approach not only supports sustainable farming but also drives economic growth through innovative solutions. ?

Why Agroxchange and Nigeria Flying Labs

Agroxchange, in partnership with EOSDA, completed a 9-hectare cassava cultivation project for CARMI Agro, a Nigerian agribusiness investor. This evidence-based pilot, which started in October 2023, highlights Agroxchange’s expertise in agtech services and crop cultivation. Agroxchange covers the full spectrum of agricultural production, from technology-driven solutions to hands-on farming.

Based on lessons learned, we are preparing key improvements for the 2025 planting season and scaling up.

1. Baseline data collection for SOC and deployment of multispectral drone for data collection at the pre-farming stage
2. Develop machine learning algorithms to train baseline data Integrate API for SOC monitoring through EOSDA, using satellite imagery such as Sentinel 2 and multispectral drone such as Parrot Blue Grass Pro, at the pre-farming stage, farming stage, and post-harvest stage.
3. Through the partnership with ESODA and Degas Africa, register the project for certification to sell carbon credits.
4. Apply regenerative agricultural practices to reduce the cost of mechanisation.

Impact-Cassava Case Study

Primary production in Nigeria has become a cost-intensive exercise, with a rise in inflation, stemming largely from the removal of oil subsidies, and leading to up to 40% food inflation. To put this in perspective, the cost of production for a hectare of cassava was about $350 in 2023 and in October 2024 at the time of harvest, the cost of production has increased to over $500 per hectare. Thus, the key impacts of the data-driven regenerative agriculture include:

1. Primary production cost offset of at least 15% from the sale of carbon credit.
2. Regenerative agriculture hedges against inflation as food prices are closely related to inflationary trends.
3. Farmers increase profitability and climate resilience.
4. Cost saving from low till or no-till land preparation practice.
5. Alignment with international carbon emission standards and initiatives such as

Koronivia Joint Work on Agriculture (KJWA) is a United Nations Framework Convention on Climate Change (UNFCCC) initiative that aims to advance discussions on agriculture and climate change.

Cassava, a staple crop in Nigeria and other parts of Africa responds positively to regenerative agriculture practices that enhance soil organic matter and improve water retention. Key regenerative practices like crop rotation, intercropping, cover cropping, and organic amendments have been found to boost cassava yields. Studies indicate that cassava yields can increase by 15-25% with the implementation of regenerative agriculture practices, depending on soil type and environmental conditions.

For example, applying organic fertilizers, such as compost or animal manure, enhances soil fertility and water-holding capacity, leading to better tuber development.

In Nigeria, cassava yields under regenerative farming systems have shown increases from an average of 12-15 tons per hectare under conventional methods to 15-25 tons per hectare with regenerative practices.

Conclusion

The integration of remote sensing for monitoring soil organic carbon is a groundbreaking innovation that aligns with the goals of regenerative agriculture and carbon sequestration.

For Nigerian farmers and agribusinesses, it offers a path toward improved productivity, environmental stewardship, and economic opportunity.

As Agroxchange leads this movement, it is positioning the country to become a global leader in sustainable agriculture and carbon management.